This invention relates to a target tracking method and a target tracking apparatus for estimating, from observed values obtained by observing a position of target such as an aircraft by a sensor such as a radar, specification data of the position and a speed of the target.
Various target tracking apparatuses are already proposed. By way of illustration, Japanese Unexamined Patent Application Publication of Tokkai No. 2000-241,539 or JP-A 2000-241539 (which will later be called a first patent document) discloses a “target tracking apparatus” which improve a tracking accuracy for a target with high mobile power by controlling an interval (sampling interval) for observing the target. The target tracking apparatus disclosed in the first patent document comprises an N motion models-based confidence calculating unit, an N motion models-based smoothing unit, an N motion models-based predictor, a motion model-by-motion model predictor, an N motion models-based smoothing error evaluator, a motion model-by-motion model prediction error evaluator, an N motion models-based prediction error evaluator, a gain matrix calculator, an observation time instant calculator, and a transition probability between motion models calculator.
The N motion models-based confidence calculator calculate an N motion models-based confidence based on motion position observed information, predicted values every motion model, prediction errors every motion model, and a confidence of each motion model before one sampling. The N motion models-based smoothing unit calculates an N motion models-based smoothed value based on the target position observed information, the N motion models-based confidence, a gain matrix, and an N motion models-based smoothed value before one sampling. The N motion models-based predictor calculates an N motion models-based predicted value based on the N motion models-based smoothed value, the N motion models-based confidence, and an observed time instant before one sampling. The motion model-by-motion model predictor calculates a motion model-by-motion model predicted value based on the N motion models-based smoothed value and the observation time instant before one sampling. The N motion models-based smoothing error evaluator calculates an N motion models-based smoothing error based on the N motion models-based confidence, the motion model-by-motion model predicted error, and the gain matrix. The motion model-by-motion model prediction error evaluator calculates a motion model-by-motion model prediction error based on the N motion models-based smoothing error and the observation time instant before one sampling. The N motion models-based prediction error evaluator calculates an N motion modules-based prediction error based on the motion model-by-motion model prediction error, the N motion models-based confidence, and the observed error before one sampling. The gain matrix calculator calculates a gain matrix based on the motion model-by-motion model prediction error. The observation time instant calculator calculates a time instant (observation time instant) when a next sampling (target observation) is carried out based on the N motion models-based confidence, the N motion models-based smoothed value, and the N motion models-based smoothed error. The transition probability between motion models calculator calculates a sampling interval between a current observation time instant and the next observation time instance based on the observation time instant produced by the observation time instant calculator to a transition probability between the motion models in the sampling interval.
At any rate, the first patent document discloses to calculate a target specification data predicted value and a target specification data error predicted value. In addition, the first patent document discloses to determine the sampling interval of observed information based on a target tracking accuracy.
In addition, Japanese Unexamined Patent Application Publication of Tokkai No. 2002-174681 or JP-A 2002-174681 (which will later be called a second patent document) discloses a “target tracking apparatus” capable of controlling a filter smoothing effect. The target tracking apparatus disclosed in FIG. 3 of the second patent document comprises a smoothing vector memory, a pseudo-smoothing error covariance matrix memory, a prediction vector calculating portion, a driving noise covariance setting portion, a pseudo-prediction error covariance matrix calculating portion, an observation noise covariance matrix setting portion, a pseudo-residual covariance matrix calculating portion, a gate decision portion, a gain matrix calculating portion, a smoothing vector calculating portion, a coefficient controlling portion, a coefficient multiplying portion, a pseudo-smoothing error covariance matrix calculating portion, a smoothing error covariance matrix memory, a prediction error covariance matrix calculating portion, a residual covariance matrix calculating portion, and a smoothing error covariance matrix calculating portion.
The smoothing vector memory stores a calculated result of a smoothing vector by the smoothing vector calculating portion. The smoothing error covariance matrix memory stores a calculated result of a smoothing error covariance matrix by the smoothing error covariance matrix calculating portion. The prediction vector calculating portion calculates a current prediction vector from a previous smoothing vector stored in the smoothing vector memory. The prediction error covariance matrix calculating portion calculates a current prediction error from the calculated result of a previous smoothing error covariance matrix stored in the smoothing error covariance matrix memory and a value of a driving noise covariance matrix set by the driving noise covariance matrix setting portion. The observation noise covariance matrix setting portion sets an observation noise covariance matrix based on parameters related to accuracy evaluation of an observation vector inputted from a target observation device. The residual covariance matrix calculating portion calculates a residual covariance matrix from the prediction error covariance matrix calculated by the prediction error covariance matrix calculating portion and the observation noise covariance matrix set by the observation noise covariance matrix setting portion. The gate decision portion calculates a residual vector based on the prediction error calculated by the prediction error calculating portion and the observation vector from the target observation device and carries out a gate decision whether or not the observation vector is detection data of a tracked target using the residual covariance matrix calculated by the residual covariance matrix calculating portion.
The pseudo-smoothing error covariance matrix memory stores a calculated result of a pseudo-smoothing error covariance matrix calculated by the pseudo-smoothing error covariance matrix calculating portion. The pseudo-prediction error covariance matrix calculating portion calculates a current pseudo-prediction error covariance matrix using a calculated result of a previous pseudo-smoothing error covariance matrix and a value of the driving noise covariance matrix set by the driving noise covariance matrix setting portion. The pseudo-residual covariance matrix calculating portion calculates a pseudo-residual covariance matrix using the pseudo-prediction error covariance matrix calculated by the pseudo-prediction error covariance matrix calculating portion and the observation noise covariance matrix set by the observation noise covariance matrix setting portion. The gain matrix calculating portion calculates a gain matrix for a filter using the pseudo-prediction error covariance matrix calculated by the pseudo-prediction error matrix calculating portion and the pseudo-residual error matrix calculated by the pseudo-residual error matrix calculating portion. The coefficient controlling portion determines a value of a scalar coefficient to be multiplexed to the observation noise covariance matrix according to a tracking state of the tracked target. The coefficient multiplying portion multiplies the observation noise covariance matrix by the above-mentioned scalar coefficient. The pseudo-smoothing error covariance matrix calculating portion calculates a pseudo-smoothing error covariance matrix using the observation noise covariance matrix multiplied by the above-mentioned scalar coefficient and the pseudo-prediction error covariance matrix calculated by the pseudo-prediction error covariance calculating portion.
At any rate, the second patent document discloses 1) to calculate the prediction vector (specification data) from the previous smoothing vector (specification data), 2) to calculate the prediction error covariance matrix from the previous error matrix, 3) to calculate the pseudo-prediction error covariance matrix (a new target specification data error predicted value) by multiplying the observation noise by the scalar coefficient based on mobility (straight-ahead, curve-ahead) of the target, 4) to calculate the gain from the pseudo-prediction error covariance matrix and do calculate the smoothing vector (estimated target) from the prediction vector and the gain, and 5) to calculate the smoothing error covariance matrix (a target specification data error estimated value) from the pseudo-prediction error covariance matrix, the observation noise, and the gain.
Japanese Unexamined Patent Application Publication of Tokkai No. 2003-130,947 or JP-A 2003-130947 (which will later be called a third patent document) discloses a “target tracking apparatus and method” which realizes a desired error elliptic body overlap degree by re-calculating a residual covariance matrix in the calculation of reliability when the motion of a target is judged to be incapable of being expressed in the model of a filter by the judgment of the overlap degree of an error elliptic body and enhances tracking capacity. In the third patent document, a predictor calculates a predicted value and a prediction error covariance matrix based on a smoothing value from a smoother, the reliability of respective motion models, a constant acceleration vector. In addition, the target tracking apparatus disclosed in the third patent document controls the error elliptic body in each axial direction to an arbitrary size to enhance tracking accuracy for a cornering target with an error reduced for a rectilinear motion target with constant velocity.
Japanese Unexamined Patent Application Publication of Tokkai No. Hei 7-35,850 or JP-A 7-35850 (which will later be called a fourth patent document) discloses a “target tracking apparatus” which ensures the processing time of computer, when a target is tracked at a short sampling interval, by calculating the predicted position and speed required for the tracking filter processing at next sampling time based on a smoothing position and speed calculated by means of a tacking filter. The fourth patent document discloses a smoothing value calculating device for calculating smoothing values of a position and a speed based on a filter gain constant and an observed value and a predicted value calculating device for calculating, from the above-mentioned smoothing values, predicted values of a position and a speed at a prediction time instant which is the next sampling time. In addition, the fourth patent document discloses a prediction vector calculating device for calculating a prediction vector at the next sampling time instant and a prediction error covariance calculating device for calculating a prediction error covariance matrix.
However, any of the first through the fourth patent documents neither discloses nor teaches maneuver detection time point prediction error adjusting units as disclosed in related target tracking apparatuses illustrated in FIGS. 5 and 7 in the manner which will later be described. In the manner which will become clear as the description proceeds, the related target tracking apparatuses are however disadvantageous in that a velocity error becomes large in accordance with a following of a delay of a course on detecting maneuver. In addition, the related target apparatuses are disadvantageous in that a course error becomes large in accordance with a following of a delay of a velocity on detecting maneuver.